The challenge with self driving cars no one talks about

We believe a cost effective part of the solution will be Earth Observation data provided by low cost nano-sats and micro-sats. It will take start-ups equivalent to the likes Planet and Spire Global with large constellations, enough satellite coverage to provide high levels of revisit at any point on earth. Maps will be enabled via satellite technologies such as SAR (Synthetic Apeture Radar) that can see ground based detail through clouds and nightfall (currently being developed by start-ups such as Iceye and Capella).

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Self-driving cars are going to need really, really good maps. But the kinds of maps we have today–like Google maps–aren’t accurate enough. While autonomous vehicles will rely on their cameras and sensors to create a picture of the world, they also need to know how to make sense of that information. That’s where a semantic map comes in–a map that continues to learn about the physical world and refine its predictions of what objects are and how they will act through huge amounts of data. Without semantic maps, self-driving cars won’t ever be able to intelligently move through the world–at least not without crashing into something–a development that will arrive when self-driving cars do (between 5 and 30 years from now, depending on whom you ask).